exploring the amihealth paradigm. monitoring in healthcare: building mhealth ecosystems
TRANSCRIPT
Exploring the AmIHEALTH paradigm
Jesús Fontecha
University of Castilla-La ManchaEscuela Superior de Informática de Ciudad Real
Ciudad Real, Spain
MAmI Research Lab
Santiago, Chile, Nov. 25-27, 2015
Summer School
Monitoring in Healthcare: Building mHealth ecosystems
2
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
CONTENT• Definition of concepts
• AmI, IoT, Healthcare, AmIHEALTH• mHealth
• Goals, Ecosystems, Limitations, Scenarios, Interoperability
• Monitoring fundamentals and study cases• Frailty monitoring• Disease monitoring• Analysis tool
• Conclusions
3
FROM AMI TO AMIHEALTHIntroduction. Definition of concepts
4
• Introduction
Internet of Things
eHealth mHealth
Smart environments & devices
Healthcare
Distributed systems
“diagnosis, treatment, and prevention of diseases and impairments in human beings”
Use of technology for supporting Healthcare
Use of mobile technology for supporting Healthcare
SensorsNetworks
Services
Devices
Embedded
AmI HEALTH
AmI
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
5
• AmIHEALTH• Ambient Intelligence for Health
More & better infrastructure- Technologies- Resources- Devices- Communication possibilities
mHEALTH AmIHEALTH
AmI
Integration of mobile technologies in Healthcare + • Context Aware
• Personalized• Anticipatory• Adaptive• Ubiquity• Transparency
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890262/
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
6
DEFINITION, GOALS, ECOSYSTEMS, LIMITATIONS, SCENARIOS, INTEROPERABILITY
mHealth
7
• mHEALTH• Mobile Health• An evolving concept• Keys
• Use of smart and mobile devices• Inclusion of wireless technology• Easy social adoption
“the delivery of healthcare services via mobile communication devices”
…are converging
Source: 2010 mHealth Summit of the Foundation for the National Institutes of Health (FNIH)
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
8
• Goals of mHEALTH solutions• Better management of health• Make better healthcare decisions• Find appropriate care
• Engage people and access providers• Management of ongoing health
(monitoring)
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
9
• Objectives for mHealth usersSource: National eHealth Collaborative
Importance (%)Objectives
• Uses of mHealth (in developed countries)• Collect data• Self monitoring (patient)• Remember events• Appointment• Remote monitoring (doctor)
Underdeveloped countries
Increasing use of mobile phones
Lack of infrastructures
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
10
• The mHealth Ecosystem
Source: Wikipedia
• “Ecosystem is a community of living organisms in conjunction with the nonliving components of their environments (things like air, water and mineral soil), interacting as a system”.
• “mHealth ecosystem is a community of people who interact with mobile devices of an environment to get clinical benefits”.
There is no standard definition for mHealth ecosystem
• Aspects to consider in development of mHealth ecosystems• Right information• Secure communication• Good system adherence & accessibility• Right time• Reduce technology impact on disease• Sustainable use of resources
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
11
• mHealth Ecosystems
Source: http://www.mdtmag.com/article/2013/05/wireless-enabled-remote-patient-monitoring-solutions
Source: H. T. Cheng and W. Zhuang, "Bluetooth-enabled in-home patient monitoring system: early detection of Alzheimer's disease," IEEE Wireless Communications, vol. 17, no. 1, pp. 74-79, Feb. 2010
Source: http://www.ece.uah.edu/~jovanov/whrms/
• But, it is not easy…
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
12
• Limitations of mHEALTH solutions and ecosystems• Technological education• Infrastructure• Communications• System Friendliness • Budget
“Everything is possible if there is an unlimited amount of time and resources”
Theoretically anything is feasible, but in practise…• There is no device that monitor this parameter!• We have commercial devices but, this API is not open!• I do not know how it works!• Here we have not network connection!• Implementation of the project is expensive! • I prefer traditional methods!• …
Scientific and technological advances improve our life quality
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
13
• Elements of a mHealth system• Users
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
14
• Elements of a mHealth system• Environment
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
15
• Elements of a mHealth system• Sensors and devices
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
16
• Elements of a mHealth system• Communication technologies
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
17
• Simulating a real scenario
Obese patient
Smartphone + smartwatch with HR monitor
Computer, tablet, smartphone
Server
Doctor
Monitoring data
Patient information
RelativesTablet, smartphone
Patie
nt d
ata
Updated data
Personal flows
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
18
• Technology convergence and consistency
Connectivity
Data
Sensors
Mobile
Users
mHealth care
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
19
• Standards and interoperability
Continua Health Alliance
• Interoperability between health devices• Fundamentals of data exchange• Define the interfaces that enable the secure flow of medical data
among sensors, gateways, and end services, removing ambiguity
Not Open APIs!
Source: http://www.continuaalliance.org/
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
20
STAGES, BIG DATA, MHEALTH SYSTEMSMonitoring fundamentals and study cases
21
• Monitoring fundamentals
Data acquisition Data segmentation & filtering Data analysis
Not only signal analysis!... Data analysis
Not only monitoring!... Many processes
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
22
• Big data• Set of mechanisms to process large amounts of data
• Data is too big, moves too fast, doesn’t fit the structures of traditional database architectures
• Characteristics• Volume. Quantity of data• Variety. Type of content• Velocity. Speed of data generation and retrieving• Variability. Deal with data inconsistency effectively• Veracity. Quality of data• Complexity. Deal with complex data management.
• Big data is very useful in data monitoring!
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
23
• Study cases• Three mHealth systems
• [Completed] Mobile system for detection and assessment of frailty syndrome in seniors. • <<Frailty monitoring>>
• [Ongoing Work] A smart and sensorized framework for continuous monitoring of diseases based on health aspects.• <<Disease monitoring>>
• [Completed] PIA: Personal IADL Assistant. Development of a web analysis tool. • <<Analysis tool>>
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
24
MOBILE SYSTEM FOR DETECTION AND ASSESSMENT OF FRAILTY SYNDROME IN SENIORS
First study case <<frailty monitoring>>
25
• Goal
Design and development of a system which uses mobile devices to provide a support to physicians in the frailty assessment, taking into account a set of relevant clinical variables and movement data.
Patient record
Accelerometer
+Clinical factors Physical activity
Analysis of patients and factors
Assessment
Adquisition
Similarity study
Tinetti test
Mobile system for detection and assessment of frailty syndrome in seniors
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
26
• Conceptual model
Mobile system for detection and assessment of frailty syndrome in seniors
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
27
• Service-oriented mHealth approach
Mobile Web
Services
- Accelerometer data acquisition- Accelerometer data processing- Visualization of frailty assessment
- Patient record extraction- Comparison and analysis procedure- Setting up a built result- Storage into patient stack
12
Mobile system for detection and assessment of frailty syndrome in seniors
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
28
• Accelerometer data acquisition and processing
Mobile system for detection and assessment of frailty syndrome in seniors
1
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
29
• Clinical factors acquisition
Mobile system for detection and assessment of frailty syndrome in seniors
2
Analysis of patients and factors
Assessment
Similarity study
Tinetti test
Anthropometric Assessment Functional AssessmentNutritional Assessment Cognitive Assessment
Geriatric Syndromes Independence in ADL Pathologies & Diseases
Gender, Age, Size, Weight, Body Mass Index, Body Mass,
Fat Mass, Lean Mass, Total Water, Drug Number
Tinetti gait score, Tinetti balnce score, Barthel index, Lawton score, Get-Up and Go, Need
help
Total protein, Serum albumin, Cholesterol level, Triglycerides,
Blood iron, Ferritin, Vitamin B12, Serum folic acid, Serum
transferrin, Leukocytes, Lymphocytes, Hemoglobin,
Calcium
Mini Mental Status, CRP
Dementia, Depression, Incontinence, Immobility,
Recurrent falls, Polypharmacy, Comorbidity, Sensory
deprivation, Pressure ulcer, Malnutrition, Terminally illness
Independent, Mild dependence, Moderate
dependence, Great dependence, Serious
dependence
Cardiovascular, Neurological, Respiratory, Digestive, Endocrine, Orthopedic,
Osteomuscular, Eyes, ENT, Dermatological
Dispersion Measures
Arithmetic mean, Standard deviation, Absolute mean diff.,
Amplitude, Pearson’s coefficient of variation,
Variance, Acceleration mean
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
30
• Clustering and similarities calculation
Mobile system for detection and assessment of frailty syndrome in seniors
3
Selection of relevant variables • Frailty risk factors
Normalization of variables
Calculation of similarity measures• Strength of relationship between 2
objects• Gower coefficient
1
2
3
Gower similarity coefficient
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
31
• System overview
Mobile system for detection and assessment of frailty syndrome in seniors
Mobile Services Infrastructure for Frailty Diagnosis Support based on Gower’s Similarity Coefficient and Treemaps. Jesús Fontecha, Ramón Hervás, José Bravo. Journal of Mobile Information System. July 2013. DOI: 10.3233/MIS-130174
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
32
• Evaluation and results summary
Mobile system for detection and assessment of frailty syndrome in seniors
• Descriptive analysis• 20 elderly people (10 men, 10 women)• 60 patient instances (data from 3 times on 20
users)• Global evaluation• More values in variables -> more accuracy• Improve decissions making frailty diagnosis by
physicians• Specific evaluation• Results adapted to different domains• Modifying some parameters in the mobile
system• Useful in evolutionary studies (nutritional &
functional)
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
33
A SMART AND SENSORIZED FRAMEWORK FOR CONTINUOUS MONITORING OF DISEASES BASED ON HEALTH ASPECTS
Second study case <<disease monitoring>>
34
• GoalDevelopment of a modular framework based on health aspects for monitoring multiple diseases by using smartphones and smart devices.
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
• Monitoring and treatment of a disease• Primary aspects
• Common to most diseases• Directly related to the disease
• Vital signs, physical activity, clinical profile, education, relatives, diet.
• Complementary aspects• Depending on the disease• Improve the monitoring
• Environment, social relationships, emotions, stress, incomes,…• Patient side (Self-control) & Doctor side (remote monitoring)• New mobile technologies, communication networks, new devices…
• New possibilities and opportunities
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
35
• Monitoring cycle
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Vital signs monitoring
Interaction
Primary aspectsComplementary aspects
Information flow
Clinical tr
eatment
Self-monitoring
Patient profile
Physician
PatientSmart devices & sensors
Smartphone
Diet
Education Relatives
Exercise
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
36
• Levels of monitoring
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Intensive
Moderate
Mild
Aspects coverage +
-
Self-care
+
-
Level 1 – Basic aspects monitoring
Level 3 – Complete monitoring
Level 2 – Usual aspects monitoring
• Adapted to the patient• Different action levels• Considered aspects
• Variables• Level of supervision• Temporary or permanent
• Some examples
Aspects
Devices & sensors
Monitoring level
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
37
• Monitoring behavior (trends and objectives)
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
• Main goal of the framework!• It depends on the disease• Objectives proposed by doctors• Trends calculated by the system (prevention!)
Diabetes
Trends Objectives• Glucose trend • Glucose level
• Physical exercise• Carbohydrate intake
Hypertension
Trends Objectives• Blood pressure
trend• Blood pressure level• Physical activity• Diet
Diabetes behavior Hypertension behavior
Examples
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
38
• Study cases. Framework overview
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
• A generic framework to deal with specific disesases
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
39
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
40
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
41
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
42
• Conclusions
A smart and sensorized framework for continuous monitoring of diseases based on health aspects
• Proposal of health aspect-based framework for smart monitoring• Monitoring of chronic and non-chronic diseases• Interaction with sensors & smart devices
• Reducing human interaction• Promoting patient self-control and remote supervision
• Future work• Development of software pieces covering
each health aspect• Deal with gathering of data from smart
devices through open API• Application to specific domain (Diabetes)
Endocrine Diet
Education
Physical activityGlucose level
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
43
PIA: PERSONAL IADL ASSISTANT. DEVELOPMENT OF A WEB ANALYSIS TOOL
Third study case <<analysis tool>>
44
• Goal & ScenarioDesign and development of a Analysis tool for a AAL system which uses mobile devices and web platforms to assess IADL of elderly people at home.
PIA: Personal IADL Assistant. Development of a web analysis tool
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
45
• How user’s information (from interaction with environmental objects and system applications) is collected?
PIA: Personal IADL Assistant. Development of a web analysis tool
System QuestionnairesEnvironmental Interaction
Caregivers and physicians complete questionnaires during the use of AAL system.
Involves recording the subjects behavior in their AAL environment.
Questionnaires collect factual information about individuals
Collecting data from user actions in the environment
Analysis tool
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
46
• Questionnaires system• Creation of questionnaires adapted
to the person
PIA: Personal IADL Assistant. Development of a web analysis tool
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
47
• Recommendations based on results• Completion of the questionnaire provides
• Recommendations• Results
PIA: Personal IADL Assistant. Development of a web analysis tool
Need of help in telephone tasks
scoreResults
Recommendations
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
48
PIA: Personal IADL Assistant. Development of a web analysis tool
• Some conclusions• The system saves information about
• Minimum interaction of elderly people with environmental objects at home (NFC technology)• Questionnaires completed by caregivers and doctors
• Information is used with analysis purposes• Evaluated on 10 caregivers• Useful to assess IADL activities in elderly people and the burden of
caregivers by means of interactions and questionnaire results
http://mamilab.esi.uclm.es/PIAToolv16/web/
Study casesMonitoringmHealthIntroductio
n
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
49
• Conclusions• mHealth as part of AmIHEALTH• Everything is possible with unlimited time and resources!• Most important element in mHealth ecosystems -> End User• Main drawbacks
• Technology• Communication networks• Environmental resources and interoperability• UX and UI
• Functional and friendly systems -> successful proposal!• Scenarios where monitoring is quite relevant• Integration of mHealth ecosystems in bigger environments (Smart
cities?)
Study casesMonitoringmHealthIntroduction
Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems
Conclusions
Exploring the AmIHEALTH paradigm
Jesús Fontechajesus.fontecha[at]uclm[dot]es
http://jesusfontecha.name
University of Castilla-La ManchaEscuela Superior de Informática de Ciudad Real
Ciudad Real, Spain
MAmI Research Lab
Santiago, Chile, Nov. 25-27, 2015
Summer School
Monitoring in Healthcare: Building mHealth ecosystems
https://www.linkedin.com/pub/jes%C3%BAs-fontecha/28/896/b98
https://www.researchgate.net/profile/Jesus_Fontecha
http://www.slideshare.net/JessFontecha/
Thank you!